A pattern of variation of a continuous variable continuous variable characterized by a symmetric bell-shaped curve.

Also called the Gaussian distribution, is the commonest of the many probability distributions.... more on: Normal distribution

A frequency distribution whose graphic representation has a symmetric, bell-shaped form — the normal curve. Its characteristics are often referred to when investigators test statistical hypotheses and make inferences about the population from a given sample.

A probability distribution, recognized as the traditional bell-shaped curve.

A continuous probability distribution commonly used in statistics and completely characterised by a mean µ, which is symmetric about, and a variance _², which describes the spread of the distribution. The distribution is denoted by N(µ_²).

A specific distribution having a characteristic bell-shaped form.

Normal distributions are a family of probability distributions that have the same general shape - usually described as bell-shaped. They are symmetric with scores more concentrated in the middle than in the tails.

Statistical term central to sampling theory. On a line chart, it shows the point at which the mean, mode, and median averages share the same value and has a characteristic ben-shaped profile. Standard deviation is calculated upon a formula derived from this distribution, enabling the confidence level (eg. 95 per cent) within which results are confined to be stated. In the example given, this would be accuracy defined to within +/-5 per cent.

The frequency of a data distribution simulating a bell-shaped curve that is symmetrical around the mean and exhibits an equal chance of a data point being above or below the mean. (syn: Gaussian distribution).

probability distribution shaped like a bell, often found in statistical samples.

See Normal Probability Distribution. [D03534

A common lifetime statistical distribution that was developed by mathematician C. F. Gauss. The distribution is a continuous, bell-shaped distribution which is symmetric about its mean and can take on values from negative infinity to positive infinity.

A theoretical distribution that is closely approximated by many actual distribution of variables.

When just about anything about people is measured - their height, for example - most people are close to the average. The further you go from the average, the fewer people have that measurement. This is sometimes called the bell-shaped curve, and a lot of statistical measures - such as standard deviation, are based on the assumption of a normal distribution. Most numeric variables in surveys follow an approximately normal distribution, with most answers near the middle of the range, and few at the extremes.

The charting of a data set in which most of the data points are concentrated around the average (mean), thus forming a bell shaped curve.

This graph shows the normal distribution of IQ scores as measured by the Wechsler Adult Intelligence Scale. The normal distribution is a type of bell-shaped frequency polygon in which most of the scores are clustered around the mean. The scores become less frequent the farther they appear above or below the mean.

A set of data consisting of things uniform in character except for one feature. Graphically represented by a normal curve.

a probability distribution in statistics, graphically displayed as a bell-shaped curve.

A continuous distribution who's p.d.f. is a symmetric bell-shaped curve. It is defined by its parameters (mean) and ( variance). We write . It arises often in nature, and can be used to approximate many other distributions.

Symmetric bell-shaped frequency distribution that can be defined by its mean and standard deviation.

A statistical measure used to calculate the probability of future returns on an investment. The distribution has a characteristic 'bell' shape illustrating that the probability of returns above the mean is identical to the probability of returns below the mean (See also Standard Deviation).

smooth bell-shaped curve symmetrical about the mean such that its shape and area obey the empirical rule.

The normal or Gaussian distribution is one of the most important probability density functions, not the least because many measurement variables have distributions that at least approximate to a normal distribution. It is usually described as bell shaped, although its exact characteristics are determined by the mean and standard deveiation. It arises when the value of a variable is determined by a large number of independent prcoesses. For example, weight is a function of many processes both genetic and environmental. Many statistical tests make the assumption that the data come from a normal distribution.

a theoretical distribution with finite mean and variance

a bell curve that extends to infinity in both directions

a distribution in which the data is concentrated around the mean

a typical bell curve, with the peak of the curve corresponding to the mean

A commonly used continuous probability distribution having a bell-shaped probability density function. Also called Gaussian. It can be used to approximate the binomial distribution as well as many other real-life distributions.

A theoretical data distribution which appears bell shaped, is symmetrical about the mean and has the most probable scores concentrated around the mean. Progressively less likely scores occur further away from the mean. 68.26% of the scores lie within one standard deviation either side of the mean, 95.44% of the scores lie within 2 standard deviations either side of the mean and 99.75% fall within three standard deviations.

results expected from surveying a large group of people. Most people will be centered around the mean, fewer people will be at the edges. An assumption for many statistical tests. Also called the bell curve.

A normal frequency distribution representing the probability that a majority of randomly selected members of a population will fall within the middle of the distribution. Represented by the bell curve.

A frequency distribution of scores that is symmetric about the mean, median, and mode.

A frequency curve in which the mean, median, and mode all have the same value

particle size distribution characterized by a bell-shaped or Gaussian distribution shape when plotted on a linear scale.1

A particular form for the distribution of a variable which, when plotted, produces a "bell" shaped curve--symmetrical, rising smoothly from a small number of cases at both extremes to a large number of cases at the middle. Not all symmetrical bell-shaped distributions meet the definition of normality. See Hayes (1973, page 296).

A bell shaped curve which has the following proper­ties: (1) a continuous symmetrical distribution; (2) the arithmetic mean, mode and median are identical; (3) its shape is completely determined by the mean and standard deviation.

Normal Distribution is a graphic representation, resembling the shape of a bell, of the frequency of scores from a test given to a population of students. Most of the scores are clustered in the middle of the score scale (or the tall arc of the bell), with a decline in the frequency of scores spread out evenly left and right from the center (or the lower curves of the bell).

An important and widely-used distribution in the field of statistics and probability. All Normal distributions are symmetric, and the mean and standard deviation values are used as its two distribution parameters.

The symmetrical clustering of values around a central location. The properties of a normal distribution include the following: (1) It is a continuous, symmetrical distribution; both tails extend to infinity; (2) the arithmetic mean, mode, and median are identical; and, (3) its shape is completely determined by the mean and standard deviation.

Statistical distribution in which data are represented by a bell-shaped curve. The distinct shape and position of the curve are determined by the mean and the standard deviation.

The well known bell shaped curve. According to the Central Limit Theorem, the probability density function of a large number of independent, identically distributed random numbers will approach the normal distribution. In the fractal family of distributions, the normal distribution only exists when alpha equals 2, or the Hurst exponent equals 0.50. Thus, the normal distribution is a special case which in time series analysis is quite rare. See: Alpha, Central Limit Theorem, Fractal Distribution.

Normal distributions are a family of distributions that are characterised by a bell-shaped, symmetric curve, with scores more concentrated in the middle than in the tails. They are defined by two parameters: the mean (, mu) and the standard deviation (, sigma). Many kinds of data are approximated well by the normal distribution. Many statistical tests assume a normal distribution. Most of these tests work well even if the distribution is only approximately normal and in many cases as long as it does not deviate greatly from normality. The normal distribution plays a vital role in inference.

a continuous distribution that is bell shaped and symmetrical about the mean.

A probability density function which characterizes many random processes. The normal curve has the form: Where, µ= The mean value of the observations s= The standard deviation of the observed values

Also called "bell curve," the normal distribution is the curved shape of a graph that is highest in the middle and lowest on the sides

The statistical distribution that appears graphically as a symmetric, bell-shaped curve. In animal breeding, the values along the horizontal axis represent the levels of performance, breeding value, etc., that are being examined in a population; the height of the curve at any point represents the relative frequency of that value in the population.

A continuous probability distribution which is used to characterize a wide variety of types of data. It is a symmetric distribution, shaped like a bell, and is completely determined by its mean and standard deviation. The normal distribution is particularly important in statistics because of the tendency for sample means to follow the normal distribution (this is a result of the Central Limit Theorem). Most classical statistics procedures such as confidence intervals rely on results from the normal distribution. The normal is also known as the Gaussian distribution after its originator, Frederich Gauss. Parameters: mean mu, standard deviation sigma0 Domain: all real X Mean: mu Variance: sigma2

Graphically represented as a bell curve. Most data has a tendency to fall into this pattern, with people clustering around the mean. The shape of this curve for a variable can be calculated from the mean and standard deviation. The characteristics of the normal distribution are that 68% of scores will be within 1 standard deviation of the mean and 95% will be within 2 standard deviations. This tendency is the basis of assumptions used in confidence interval estimation and hypothesis testing.

aka "bell-shaped" distribution, a convenient description of the number of runners finishing as a function of finish time.

A continuous probability distribution whose probability density function has a "bell" shape.

(noun) A probability distribution forming a symmetrical bell-shaped curve.

Data following a normal distribution, such that a bell-shaped curve.

The spread of data on a graph showing the normal bell-shaped curve which is assumed to be a continuous frequency distribution of infinite range.

A normal random variable has a "bell shaped" PDF.

A distribution of scores (1) that is symmetrical around the mean, (2) where the mean, median, and mode are the same, and (3) where the plotted frequency distribution appears bell-shaped. When these assumptions are satisfied (even approximately) it is possible to make use of more advanced inferential statistics that make use of the known statistical properties of the normal distribution.

A symmetric, bell-shaped probability distribution with mean and standard deviation. If observations follow a normal distribution, the interval (mean + 2SDs) contains 95% of the observations. It is also called the Gaussian Distribution.

Any of a family of bell-shaped frequency curves whose relative position and shape are defined on the basis of the mean and standard deviation.

The familiar bell-shaped distribution. Simple statistics assumes that random errors are distributed in this distribution. Also called Gaussian Distribution.

The distribution associated with most sets of real-world data. Due to the shape of this distribution, it is also famously called the "bell curve."

Normal distribution is the spread of information (such as product performance or demographics) where the most frequently occurring value is in the middle of the range and other probabilities tail off symmetrically in both directions. Normal distribution is graphically categorized by a bell-shaped curve, also known as a Gaussian distribution. For normally distributed data, the mean and median are very close and may be identical.

Perhaps the most important probability distribution for probability and statistics.

A statistical mode of calculating the future profitability of an investment.

A model of an ideal test score distributions approximate. It is symmetrical and bell-shaped, with most scores clustered around the mean, with diminishing frequencies of scores further away from the mean in either direction. Unfortunately named since it suggests that any score distributions that don’t conform to the model are “abnormal” whereas they are probably not

For the purposes of statistical testing, the simulated net returns are assumed to be drawn from a particular distribution. If net returns are drawn from a normal distribution, low and high returns are equally likely, and the most likely net return in a quarter is the average net return.

The standard symmetrical bell-shaped frequency distribution, whose properties are commonly used in making statistical inferences from measures derived from samples. See also normal curve.

The fundamental frequency distribution of statistical analysis. A continuous variety is said to have a normal distribution or to be normally distributed if it possesses a density function f(x) which satisfies the equation where μ is the arithmetic mean (or first moment) and σ is the standard deviation. Also called Gaussian distribution. About two-thirds of the total area under the curve is included between x = μ - σ and x = μ + σ. The corresponding frequency distribution of vectors is the normal circular distribution in which the frequencies of vector deviations are represented by a series of circles centered on a vector mean. When applied to error distribution, this function is the normal law of errors, and the distribution is called the normal curve of error.

A particular statistical distribution where most of the observations fall fairly close to one mean, and a deviation from the mean is as likely to be plus as it is to be minus. When graphed, the normal distribution takes the form of a bell-shaped curve.

M_X(t)= \exp\left(\mu\,t+\frac{\sigma^2 t^2}{2}\right)| char =\chi_X(t)=\exp\left(\mu\,i\,t-\frac{\sigma^2 t^2}{2}\right)| }} The normal distribution, also called Gaussian distribution by scientists (named after Carl Friedrich Gauss due to his rigorous application of the distribution to astronomical data (Havil, 2003)) is a probability distribution of great importance in many fields. It is a family of distributions of the same general form, differing in their location and scale parameters: the mean ("average") and standard deviation ("variability"), respectively. The standard normal distribution is the normal distribution with a mean of zero and a variance of one (the green curves in the plots to the right).